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T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
- Source :
- DAC
- Publication Year :
- 2020
-
Abstract
- Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their applicability in practical machine learning problems. The deep neural network-to-SNN conversion approach has been widely studied to broaden the applicability of SNNs. Most previous studies, however, have not fully utilized spatio-temporal aspects of SNNs, which has led to inefficiency in terms of number of spikes and inference latency. In this paper, we present T2FSNN, which introduces the concept of time-to-first-spike coding into deep SNNs using the kernel-based dynamic threshold and dendrite to overcome the aforementioned drawback. In addition, we propose gradient-based optimization and early firing methods to further increase the efficiency of the T2FSNN. According to our results, the proposed methods can reduce inference latency and number of spikes to 22% and less than 1%, compared to those of burst coding, which is the state-of-the-art result on the CIFAR-100.<br />Accepted to DAC 2020
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Inference
Dendrite
Machine Learning (stat.ML)
02 engineering and technology
010501 environmental sciences
01 natural sciences
Machine Learning (cs.LG)
Statistics - Machine Learning
0202 electrical engineering, electronic engineering, information engineering
medicine
Neural and Evolutionary Computing (cs.NE)
0105 earth and related environmental sciences
Spiking neural network
Contextual image classification
business.industry
Supervised learning
Computer Science - Neural and Evolutionary Computing
Pattern recognition
medicine.anatomical_structure
020201 artificial intelligence & image processing
Artificial intelligence
business
Coding (social sciences)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- DAC
- Accession number :
- edsair.doi.dedup.....397cc2be2b0651e66bb3d7c341ca1be9